{
 "metadata": {
  "name": "",
  "signature": "sha256:277a241f111d2ab7b8f700261b6274a242374dda4270469fc4bb03d1c0949afe"
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Evolution of Airports, Airways in the United States"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Team Members"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Anusha Ramamurthy\n",
      "Sathosh Soundararajan"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Abstract"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An article to create a time series analysis of the airline industry, visualizing the onset of different airports and airline carriers at various locations across the United States. It will also feature the performance of top airlines based on their flight on-time arrivals and departures. Further it will explore the various factors that cause delays and predict them. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Why should we care?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The United States has an extensive air transportation network. In 2013 there were 86 airports in the U.S. that annually handled over 1,000,000 passengers and twelve of the world's thirty busiest airports by passenger volume in 2014 were in the U.S.[Wikipedia]\n",
      "\n",
      "Air Travel is the most practical solution in the US, given the vastness of the country. However, this form of travel is not without problems. The entire system is both fascinating and disturbing, given the fact that overall, passengers were delayed 320 million hours in 2007 with an estimated deadweight economic loss of up to $41 billion[Wikipedia]. \n",
      "\n",
      "We wish to depict how this complex system has evolved over the years and how, due to the delays, the system needs to be changed and improved."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Main Message"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "summarize the main message, outline the document, show preliminary visualizations you have already created, and show some sketches of visualizations that you want to create.\n",
      "\n",
      "Our goal is to visualize this complex yet streamlined system of air transportation in the United States. We will depict a time series storyboard of the airline industry."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Outlines"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "    1. Time Series Visualization of Evolution of Airports.\n",
      "    2. A Dorling Cartogram of the density of airports across US.\n",
      "    3. Time Series Visualization the onset of airline carriers across US. \n",
      "    4. Performance analysis of airlines. \n",
      "    5. Visualization of the factors that contribute to the delay.   "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Preliminary visualizations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We wish to show the evolution of airports across the US. Our current dataset has airline industry data spanning over 21 years, from the year 1987-2008. We will depict this with a US map which animates the presence of airports between these years. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.image as mpimg\n",
      "import matplotlib.pyplot as plt\n",
      "img = mpimg.imread('/home/anusha/Downloads/DV-Final project/prelim_2.jpg')            #a picture of preliminary visualizations sketches\n",
      "plt.imshow(img)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f2c27be0d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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x1d3DoRliJLR7VBtxZYm0iag1MQYQHmsWWHu0qAfI0qOsJUfPMLQIPUNrTd/3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       "text": [
        "<matplotlib.figure.Figure at 0x7f2c27d5b910>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note: Please find attached the image, as it is not rendered correctly here."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Background and related work:\n",
      "    \n",
      "#####DataSet:   \n",
      "http://stat-computing.org/dataexpo/2009/the-data.html\n",
      "This dataset is a collection of yearly data of 21 years starting from 1987 to 2008. \n",
      "\n",
      "https://developer.flightstats.com\n",
      "This is a SOAP based data scraping system/API we planned to retrive data for a given set of conditions. \n",
      "\n",
      "#####Related work \n",
      "We would like to share some interesting visualizations that appeared on some of the majors websites concerned with flight data but mostly concentrating on flight and carrier performances, based on flight timings. However,we mainly explore the evolution of air ways as a whole. \n",
      "\n",
      "http://projects.fivethirtyeight.com/flights/\n",
      "\n",
      "https://flowingdata.com/2009/09/10/3-in-depth-views-of-flight-delays-and-cancellations/\n",
      "\n",
      "http://thenextweb.com/shareables/2015/02/18/visualization-worlds-airline-flight-traffic-mesmerizing/\n",
      "\n",
      "#####Related Paper:\n",
      "http://www.usna.edu/Users/math/dphillip/jcgs-july.pdf\n",
      "\n",
      "\n",
      "#####Further Work:\n",
      "\n",
      "We would like to visualize an interactive module to find the best possible routes between two given airports and predict delays."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}